Dureja Harish, Madan Anil Kumar
Faculty of Pharmaceutical Sciences, M. D. University, Rohtak-124001, India.
Acta Pharm. 2007 Dec;57(4):451-67. doi: 10.2478/v10007-007-0036-2.
Recently published topochemical models for permeability through the blood-brain barrier were validated and cross-validated in the present study. Five models based on three topochemical indices, Wiener's topochemical index - a distance-based topochemical descriptor, molecular connectivity topochemical index - an adjacency-based topochemical descriptor and eccentric connectivity topochemical index - an adjacency-cum-distance based topochemical descriptor, for permeability of structurally and chemically diverse molecules through blood-brain barrier were used in the present investigation. A data set comprising 62 structurally and chemically diverse compounds was selected. This data set was divided into two sets of 31 compounds each - one to serve as the validation set and other as the cross-validation set. The values of all the three-topochemical indices in the original as well as in the normalized form for each of the 31 compounds of the validation set were computed using an in-house computer program. Resultant data was analyzed and each compound was assigned a permeability characteristic using topochemical models, which was then compared with the reported permeability through the blood-brain barrier. Accuracy of prediction of these models was calculated. The same procedure was similarly followed for the cross-validation set. Studies revealed accuracy of prediction of the order of 70-80% during validation. Surprisingly, very high predictability of the order of 77-91% was observed during cross-validation. High predictability observed during validation as well as cross-validation authenticates topochemical models for prediction of permeability through the blood-brain barrier.
最近发表的关于血脑屏障通透性的拓扑化学模型在本研究中得到了验证和交叉验证。本研究使用了基于三种拓扑化学指标的五个模型,即维纳拓扑化学指标(一种基于距离的拓扑化学描述符)、分子连接性拓扑化学指标(一种基于邻接性的拓扑化学描述符)和偏心连接性拓扑化学指标(一种基于邻接性和距离的拓扑化学描述符),用于预测结构和化学性质各异的分子透过血脑屏障的通透性。选择了一个包含62种结构和化学性质各异的化合物的数据集。该数据集被分为两组,每组31种化合物,一组用作验证集,另一组用作交叉验证集。使用内部计算机程序计算验证集中31种化合物中每种化合物的原始形式和归一化形式的所有三种拓扑化学指标的值。对所得数据进行分析,并使用拓扑化学模型为每种化合物指定通透性特征,然后将其与报道的透过血脑屏障的通透性进行比较。计算这些模型的预测准确性。交叉验证集也采用相同的程序。研究表明,在验证过程中预测准确性约为70 - 80%。令人惊讶的是,在交叉验证过程中观察到了高达77 - 91%的非常高的可预测性。在验证和交叉验证过程中观察到的高可预测性证实了用于预测透过血脑屏障通透性的拓扑化学模型。